Explainable machine learning in materials science

Zhong, Xiaoting; Gallagher, Brian; Liu, Shusen; Kailkhura, Bhavya; Hiszpanski, Anna; Han, T. Yong-Jin

doi:10.1038/s41524-022-00884-7

Title: Explainable machine learning in materials science

Journal Article · 22 September 2022 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-022-00884-7 · OSTI ID:1888837

; Gallagher, Brian;

; Kailkhura, Bhavya;

;

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1888837

Report Number(s):: LLNL--JRNL-833293; 204; PII: 884

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 8; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (115)

Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films Griffin, Lee A.; Gaponenko, Iaroslav; Bassiri‐Gharb, Nazanin Advanced Materials, Vol. 32, Issue 38 https://doi.org/10.1002/adma.202002425	journal	August 2020
Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties Liu, Yue; Wu, Jun‐Ming; Avdeev, Maxim Advanced Theory and Simulations, Vol. 3, Issue 2 https://doi.org/10.1002/adts.201900215	journal	January 2020
A Survey on Deep Transfer Learning Tan, Chuanqi; Sun, Fuchun; Kong, Tao Artificial Neural Networks and Machine Learning – ICANN 2018 https://doi.org/10.1007/978-3-030-01424-7_27	book	January 2018
The (Un)reliability of Saliency Methods Kindermans, Pieter-Jan; Hooker, Sara; Adebayo, Julius Explainable AI: Interpreting, Explaining and Visualizing Deep Learning https://doi.org/10.1007/978-3-030-28954-6_14	book	January 2019
Quantum-Chemical Insights from Interpretable Atomistic Neural Networks Schütt, Kristof T.; Gastegger, Michael; Tkatchenko, Alexandre Explainable AI: Interpreting, Explaining and Visualizing Deep Learning https://doi.org/10.1007/978-3-030-28954-6_17	book	January 2019
Gradient-Based Attribution Methods Ancona, Marco; Ceolini, Enea; Öztireli, Cengiz Explainable AI: Interpreting, Explaining and Visualizing Deep Learning https://doi.org/10.1007/978-3-030-28954-6_9	book	January 2019
DeepRED – Rule Extraction from Deep Neural Networks Zilke, Jan Ruben; Loza Mencía, Eneldo; Janssen, Frederik Discovery Science https://doi.org/10.1007/978-3-319-46307-0_29	book	January 2016
Definitions and properties of zero-knowledge proof systems Goldreich, Oded; Oren, Yair Journal of Cryptology, Vol. 7, Issue 1 https://doi.org/10.1007/BF00195207	journal	December 1994
A theory of learning from different domains Ben-David, Shai; Blitzer, John; Crammer, Koby Machine Learning, Vol. 79, Issue 1-2 https://doi.org/10.1007/s10994-009-5152-4	journal	October 2009
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems Augasta, M. Gethsiyal; Kathirvalavakumar, T. Neural Processing Letters, Vol. 35, Issue 2 https://doi.org/10.1007/s11063-011-9207-8	journal	December 2011
Visualizing Deep Convolutional Neural Networks Using Natural Pre-images Mahendran, Aravindh; Vedaldi, Andrea International Journal of Computer Vision, Vol. 120, Issue 3 https://doi.org/10.1007/s11263-016-0911-8	journal	May 2016
A Comparative Study of Feature Selection Methods for Stress Hotspot Classification in Materials Mangal, Ankita; Holm, Elizabeth A. Integrating Materials and Manufacturing Innovation, Vol. 7, Issue 3 https://doi.org/10.1007/s40192-018-0109-8	journal	June 2018
Microstructure Cluster Analysis with Transfer Learning and Unsupervised Learning Kitahara, Andrew R.; Holm, Elizabeth A. Integrating Materials and Manufacturing Innovation, Vol. 7, Issue 3 https://doi.org/10.1007/s40192-018-0116-9	journal	August 2018
Benchmark AFLOW Data Sets for Machine Learning Clement, Conrad L.; Kauwe, Steven K.; Sparks, Taylor D. Integrating Materials and Manufacturing Innovation, Vol. 9, Issue 2 https://doi.org/10.1007/s40192-020-00174-4	journal	May 2020
Informing Mechanical Model Development Using Lower-Dimensional Descriptions of Lattice Distortion Pagan, Darren C.; Schmidt, Gideon H.; Borum, Andy D. Integrating Materials and Manufacturing Innovation, Vol. 9, Issue 4 https://doi.org/10.1007/s40192-020-00196-y	journal	December 2020
Occam's Razor Blumer, Anselm; Ehrenfeucht, Andrzej; Haussler, David Information Processing Letters, Vol. 24, Issue 6 https://doi.org/10.1016/0020-0190(87)90114-1	journal	April 1987
Survey and critique of techniques for extracting rules from trained artificial neural networks Andrews, Robert; Diederich, Joachim; Tickle, Alan B. Knowledge-Based Systems, Vol. 8, Issue 6 https://doi.org/10.1016/0950-7051(96)81920-4	journal	December 1995
Influence of microstructure on the ionic conductivity of yttria-stabilized zirconia electrolyte Chen, X. J.; Khor, K. A.; Chan, S. H. Materials Science and Engineering: A, Vol. 335, Issue 1-2 https://doi.org/10.1016/S0921-5093(01)01935-9	journal	September 2002
Twinning-related grain boundary engineering Randle, Valerie Acta Materialia, Vol. 52, Issue 14 https://doi.org/10.1016/j.actamat.2004.05.031	journal	August 2004
Microstructure reconstructions from 2-point statistics using phase-recovery algorithms Fullwood, David T.; Niezgoda, Stephen R.; Kalidindi, Surya R. Acta Materialia, Vol. 56, Issue 5 https://doi.org/10.1016/j.actamat.2007.10.044	journal	March 2008
Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics Kondo, Ruho; Yamakawa, Shunsuke; Masuoka, Yumi Acta Materialia, Vol. 141 https://doi.org/10.1016/j.actamat.2017.09.004	journal	December 2017
Material structure-property linkages using three-dimensional convolutional neural networks Cecen, Ahmet; Dai, Hanjun; Yabansu, Yuksel C. Acta Materialia, Vol. 146 https://doi.org/10.1016/j.actamat.2017.11.053	journal	March 2018
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches Yang, Zijiang; Yabansu, Yuksel C.; Jha, Dipendra Acta Materialia, Vol. 166 https://doi.org/10.1016/j.actamat.2018.12.045	journal	March 2019
Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening Zhang, Hongtao; Fu, Huadong; He, Xingqun Acta Materialia, Vol. 200 https://doi.org/10.1016/j.actamat.2020.09.068	journal	November 2020
A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness Yang, Chen; Ren, Chang; Jia, Yuefei Acta Materialia, Vol. 222 https://doi.org/10.1016/j.actamat.2021.117431	journal	January 2022
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques Kadeethum, T.; Ballarin, F.; Choi, Y. Advances in Water Resources, Vol. 160 https://doi.org/10.1016/j.advwatres.2021.104098	journal	February 2022
Explanation in artificial intelligence: Insights from the social sciences Miller, Tim Artificial Intelligence, Vol. 267 https://doi.org/10.1016/j.artint.2018.07.007	journal	February 2019
Physics-aware Gaussian processes in remote sensing Camps-Valls, Gustau; Martino, Luca; Svendsen, Daniel H. Applied Soft Computing, Vol. 68 https://doi.org/10.1016/j.asoc.2018.03.021	journal	July 2018
Advanced microstructure classification by data mining methods Gola, Jessica; Britz, Dominik; Staudt, Thorsten Computational Materials Science, Vol. 148 https://doi.org/10.1016/j.commatsci.2018.03.004	journal	June 2018
MatCALO: Knowledge-enabled machine learning in materials science Picklum, Mareike; Beetz, Michael Computational Materials Science, Vol. 163 https://doi.org/10.1016/j.commatsci.2019.03.005	journal	June 2019
Machine learning in materials science: From explainable predictions to autonomous design Pilania, Ghanshyam Computational Materials Science, Vol. 193 https://doi.org/10.1016/j.commatsci.2021.110360	journal	June 2021
Methods for interpreting and understanding deep neural networks Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert Digital Signal Processing, Vol. 73 https://doi.org/10.1016/j.dsp.2017.10.011	journal	February 2018
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI Barredo Arrieta, Alejandro; Díaz-Rodríguez, Natalia; Del Ser, Javier Information Fusion, Vol. 58 https://doi.org/10.1016/j.inffus.2019.12.012	journal	June 2020
Notions of explainability and evaluation approaches for explainable artificial intelligence Vilone, Giulia; Longo, Luca Information Fusion, Vol. 76 https://doi.org/10.1016/j.inffus.2021.05.009	journal	December 2021
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Journal of Computational Physics, Vol. 378 https://doi.org/10.1016/j.jcp.2018.10.045	journal	February 2019
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime Grigo, Constantin; Koutsourelakis, Phaedon-Stelios Journal of Computational Physics, Vol. 397 https://doi.org/10.1016/j.jcp.2019.05.053	journal	November 2019
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder Kim, Youngkyu; Choi, Youngsoo; Widemann, David Journal of Computational Physics, Vol. 451 https://doi.org/10.1016/j.jcp.2021.110841	journal	February 2022
Hall-Petch relationship in Mg alloys: A review Yu, Huihui; Xin, Yunchang; Wang, Maoyin Journal of Materials Science & Technology, Vol. 34, Issue 2 https://doi.org/10.1016/j.jmst.2017.07.022	journal	February 2018
Predicting compressive strength of consolidated molecular solids using computer vision and deep learning Gallagher, Brian; Rever, Matthew; Loveland, Donald Materials & Design, Vol. 190 https://doi.org/10.1016/j.matdes.2020.108541	journal	May 2020
Building data-driven models with microstructural images: Generalization and interpretability Ling, Julia; Hutchinson, Maxwell; Antono, Erin Materials Discovery, Vol. 10 https://doi.org/10.1016/j.md.2018.03.002	journal	December 2017
Learning acoustic emission signatures from a nanoindentation-based lithography process: Towards rapid microstructure characterization Iquebal, Ashif Sikandar; Pandagare, Shirish; Bukkapatnam, Satish Tribology International, Vol. 143 https://doi.org/10.1016/j.triboint.2019.106074	journal	March 2020
Towards better analysis of machine learning models: A visual analytics perspective Liu, Shixia; Wang, Xiting; Liu, Mengchen Visual Informatics, Vol. 1, Issue 1 https://doi.org/10.1016/j.visinf.2017.01.006	journal	March 2017
Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM): From Scanning Nanodiffraction to Ptychography and Beyond Ophus, Colin Microscopy and Microanalysis, Vol. 25, Issue 3 https://doi.org/10.1017/S1431927619000497	journal	May 2019
Interpretable and Explainable Machine Learning for Materials Science and Chemistry Oviedo, Felipe; Ferres, Juan Lavista; Buonassisi, Tonio Accounts of Materials Research, Vol. 3, Issue 6 https://doi.org/10.1021/accountsmr.1c00244	journal	June 2022
Machine Learning and Statistical Analysis for Materials Science: Stability and Transferability of Fingerprint Descriptors and Chemical Insights Pankajakshan, Praveen; Sanyal, Suchismita; de Noord, Onno E. Chemistry of Materials, Vol. 29, Issue 10 https://doi.org/10.1021/acs.chemmater.6b04229	journal	May 2017
SchNetPack: A Deep Learning Toolbox For Atomistic Systems Schütt, K. T.; Kessel, P.; Gastegger, M. Journal of Chemical Theory and Computation, Vol. 15, Issue 1 https://doi.org/10.1021/acs.jctc.8b00908	journal	November 2018
Deep Learning for Optoelectronic Properties of Organic Semiconductors Lu, Chengqiang; Liu, Qi; Sun, Qiming The Journal of Physical Chemistry C, Vol. 124, Issue 13 https://doi.org/10.1021/acs.jpcc.0c00329	journal	March 2020
Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction Schwaller, Philippe; Laino, Teodoro; Gaudin, Théophile ACS Central Science, Vol. 5, Issue 9 https://doi.org/10.1021/acscentsci.9b00576	journal	August 2019
Leveraging Uncertainty from Deep Learning for Trustworthy Material Discovery Workflows Zhang, Jize; Kailkhura, Bhavya; Han, T. Yong-Jin ACS Omega, Vol. 6, Issue 19 https://doi.org/10.1021/acsomega.1c00975	journal	May 2021
Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis Liu, Shusen; Kailkhura, Bhavya; Zhang, Jize ACS Omega, Vol. 7, Issue 3 https://doi.org/10.1021/acsomega.1c04796	journal	January 2022
Sinterability of commercial 8 mol% yttria-stabilized zirconia powders and the effect of sintered density on the ionic conductivity Gibson, I. R.; Dransfield, G. P.; Gibson, I. R. Journal of Materials Science, Vol. 33, Issue 17 https://doi.org/10.1023/A:1004435504482	journal	September 1998
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
Quantum-chemical insights from deep tensor neural networks Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms13890	journal	January 2017
Insightful classification of crystal structures using deep learning Ziletti, Angelo; Kumar, Devinder; Scheffler, Matthias Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05169-6	journal	July 2018
State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis Tetko, Igor V.; Karpov, Pavel; Van Deursen, Ruud Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-19266-y	journal	November 2020
Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias Kovács, Dávid Péter; McCorkindale, William; Lee, Alpha A. Nature Communications, Vol. 12, Issue 1 https://doi.org/10.1038/s41467-021-21895-w	journal	March 2021
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials Rovinelli, Andrea; Sangid, Michael D.; Proudhon, Henry npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0094-7	journal	July 2018
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks Oviedo, Felipe; Ren, Zekun; Sun, Shijing npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0196-x	journal	May 2019
Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods Ludwig, Alfred npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0205-0	journal	July 2019
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019
Identification of advanced spin-driven thermoelectric materials via interpretable machine learning Iwasaki, Yuma; Sawada, Ryohto; Stanev, Valentin npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0241-9	journal	October 2019
Reliable and explainable machine-learning methods for accelerated material discovery Kailkhura, Bhavya; Gallagher, Brian; Kim, Sookyung npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0248-2	journal	November 2019
Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls Holstad, Theodor S.; Ræder, Trygve M.; Evans, Donald M. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00426-z	journal	October 2020
Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses Lu, Zhichao; Chen, Xin; Liu, Xiongjun npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-00460-x	journal	December 2020
Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules Pei, Zongrui; Yin, Junqi; Hawk, Jeffrey A. npj Computational Materials, Vol. 6, Issue 1 https://doi.org/10.1038/s41524-020-0308-7	journal	May 2020
Compositionally restricted attention-based network for materials property predictions Wang, Anthony Yu-Tung; Kauwe, Steven K.; Murdock, Ryan J. npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00545-1	journal	May 2021
A study of real-world micrograph data quality and machine learning model robustness Zhong, Xiaoting; Gallagher, Brian; Eves, Keenan npj Computational Materials, Vol. 7, Issue 1 https://doi.org/10.1038/s41524-021-00616-3	journal	October 2021
Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy Hsu, Tim; Pham, Tuan Anh; Keilbart, Nathan npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00841-4	journal	July 2022
High-throughput calculations of magnetic topological materials Xu, Yuanfeng; Elcoro, Luis; Song, Zhi-Da Nature, Vol. 586, Issue 7831 https://doi.org/10.1038/s41586-020-2837-0	journal	October 2020
Highly accurate protein structure prediction with AlphaFold Jumper, John; Evans, Richard; Pritzel, Alexander Nature https://doi.org/10.1038/s41586-021-03819-2	journal	July 2021
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition Jha, Dipendra; Ward, Logan; Paul, Arindam Scientific Reports, Vol. 8, Issue 1 https://doi.org/10.1038/s41598-018-35934-y	journal	December 2018
Machine-learning guided discovery of a new thermoelectric material Iwasaki, Yuma; Takeuchi, Ichiro; Stanev, Valentin Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-39278-z	journal	February 2019
Physics-informed machine learning Karniadakis, George Em; Kevrekidis, Ioannis G.; Lu, Lu Nature Reviews Physics, Vol. 3, Issue 6 https://doi.org/10.1038/s42254-021-00314-5	journal	May 2021
Mapping the space of chemical reactions using attention-based neural networks Schwaller, Philippe; Probst, Daniel; Vaucher, Alain C. Nature Machine Intelligence, Vol. 3, Issue 2 https://doi.org/10.1038/s42256-020-00284-w	journal	January 2021
Perspective: Machine learning potentials for atomistic simulations Behler, Jörg The Journal of Chemical Physics, Vol. 145, Issue 17 https://doi.org/10.1063/1.4966192	journal	November 2016
SchNet – A deep learning architecture for molecules and materials Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019779	journal	June 2018
The Deformation and Ageing of Mild Steel: III Discussion of Results Hall, E. O. Proceedings of the Physical Society. Section B, Vol. 64, Issue 9 https://doi.org/10.1088/0370-1301/64/9/303	journal	September 1951
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties Schütt, K. T.; Glawe, H.; Brockherde, F. Physical Review B, Vol. 89, Issue 20 https://doi.org/10.1103/PhysRevB.89.205118	journal	May 2014
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces Li, Zhenwei; Kermode, James R.; De Vita, Alessandro Physical Review Letters, Vol. 114, Issue 9 https://doi.org/10.1103/PhysRevLett.114.096405	journal	March 2015
Big Data of Materials Science: Critical Role of the Descriptor Ghiringhelli, Luca M.; Vybiral, Jan; Levchenko, Sergey V. Physical Review Letters, Vol. 114, Issue 10 https://doi.org/10.1103/PhysRevLett.114.105503	journal	March 2015
SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates Ouyang, Runhai; Curtarolo, Stefano; Ahmetcik, Emre Physical Review Materials, Vol. 2, Issue 8 https://doi.org/10.1103/PhysRevMaterials.2.083802	journal	August 2018
A database for handwritten text recognition research Hull, J. J. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, Issue 5 https://doi.org/10.1109/34.291440	journal	May 1994
ANN-DT: an algorithm for extraction of decision trees from artificial neural networks Schmitz, G. P. J.; Aldrich, C.; Gouws, F. S. IEEE Transactions on Neural Networks, Vol. 10, Issue 6 https://doi.org/10.1109/72.809084	journal	January 1999
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) Adadi, Amina; Berrada, Mohammed IEEE Access, Vol. 6 https://doi.org/10.1109/ACCESS.2018.2870052	journal	January 2018
Explainable Machine Learning for Scientific Insights and Discoveries Roscher, Ribana; Bohn, Bastian; Duarte, Marco F. IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.2976199	journal	January 2020
Explaining CNN and RNN Using Selective Layer-Wise Relevance Propagation Jung, Yeon-Jee; Han, Seung-Ho; Choi, Ho-Jin IEEE Access, Vol. 9 https://doi.org/10.1109/ACCESS.2021.3051171	journal	January 2021
ImageNet: A large-scale hierarchical image database Deng, Jia; Dong, Wei; Socher, Richard 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 2009 IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2009.5206848	conference	June 2009
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images Nguyen, Anh; Yosinski, Jason; Clune, Jeff 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2015.7298640	conference	June 2015
Deep filter banks for texture recognition and segmentation Cimpoi, Mircea; Maji, Subhransu; Vedaldi, Andrea 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2015.7299007	conference	June 2015
Learning Deep Features for Discriminative Localization Zhou, Bolei; Khosla, Aditya; Lapedriza, Agata 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.319	conference	June 2016
Network Dissection: Quantifying Interpretability of Deep Visual Representations Bau, David; Zhou, Bolei; Khosla, Aditya 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2017.354	conference	July 2017
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space Nguyen, Anh; Clune, Jeff; Bengio, Yoshua 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2017.374	conference	July 2017
Interpretable Convolutional Neural Networks Zhang, Quanshi; Wu, Ying Nian; Zhu, Song-Chun 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2018.00920	conference	June 2018
Explainability Methods for Graph Convolutional Neural Networks Pope, Phillip E.; Kolouri, Soheil; Rostami, Mohammad 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2019.01103	conference	June 2019
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition Razavian, Ali Sharif; Azizpour, Hossein; Sullivan, Josephine 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) https://doi.org/10.1109/CVPRW.2014.131	conference	June 2014
Explaining Explanations: An Overview of Interpretability of Machine Learning Gilpin, Leilani H.; Bau, David; Yuan, Ben Z. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) https://doi.org/10.1109/DSAA.2018.00018	conference	October 2018
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization Selvaraju, Ramprasaath R.; Cogswell, Michael; Das, Abhishek 2017 IEEE International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2017.74	conference	October 2017
Attention Augmented Convolutional Networks Bello, Irwan; Zoph, Barret; Le, Quoc 2019 IEEE/CVF International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2019.00338	conference	October 2019
AttGAN: Facial Attribute Editing by Only Changing What You Want He, Zhenliang; Zuo, Wangmeng; Kan, Meina IEEE Transactions on Image Processing, Vol. 28, Issue 11 https://doi.org/10.1109/TIP.2019.2916751	journal	November 2019
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data Karpatne, Anuj; Atluri, Gowtham; Faghmous, James H. IEEE Transactions on Knowledge and Data Engineering, Vol. 29, Issue 10 https://doi.org/10.1109/TKDE.2017.2720168	journal	October 2017
Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems von Rueden, Laura; Mayer, Sebastian; Beckh, Katharina IEEE Transactions on Knowledge and Data Engineering https://doi.org/10.1109/TKDE.2021.3079836	journal	January 2021
Evaluating the Visualization of What a Deep Neural Network Has Learned Samek, Wojciech; Binder, Alexander; Montavon, Gregoire IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, Issue 11 https://doi.org/10.1109/TNNLS.2016.2599820	journal	November 2017
Distilling Free-Form Natural Laws from Experimental Data Schmidt, Michael; Lipson, Hod Science, Vol. 324, Issue 5923 https://doi.org/10.1126/science.1165893	journal	April 2009
The WEKA data mining software: an update Hall, Mark; Frank, Eibe; Holmes, Geoffrey ACM SIGKDD Explorations Newsletter, Vol. 11, Issue 1 https://doi.org/10.1145/1656274.1656278	journal	November 2009
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Ribeiro, Marco Tulio; Singh, Sameer; Guestrin, Carlos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 https://doi.org/10.1145/2939672.2939778	conference	January 2016
XGBoost: A Scalable Tree Boosting System Chen, Tianqi; Guestrin, Carlos Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16 https://doi.org/10.1145/2939672.2939785	conference	January 2016
A Survey of Methods for Explaining Black Box Models Guidotti, Riccardo; Monreale, Anna; Ruggieri, Salvatore ACM Computing Surveys, Vol. 51, Issue 5 https://doi.org/10.1145/3236009	journal	January 2019
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery. Lipton, Zachary C. Queue, Vol. 16, Issue 3 https://doi.org/10.1145/3236386.3241340	journal	June 2018
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) Breiman, Leo Statistical Science, Vol. 16, Issue 3 https://doi.org/10.1214/ss/1009213726	journal	August 2001
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation Bach, Sebastian; Binder, Alexander; Montavon, Grégoire PLOS ONE, Vol. 10, Issue 7 https://doi.org/10.1371/journal.pone.0130140	journal	July 2015
A Decomposable Attention Model for Natural Language Inference Parikh, Ankur; Täckström, Oscar; Das, Dipanjan Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing https://doi.org/10.18653/v1/D16-1244	conference	January 2016
Feature Visualization Olah, Chris; Mordvintsev, Alexander; Schubert, Ludwig Distill, Vol. 2, Issue 11 https://doi.org/10.23915/distill.00007	journal	November 2017
Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches Aamodt, Agnar; Plaza, Enric AI Communications, Vol. 7, Issue 1 https://doi.org/10.3233/AIC-1994-7104	journal	January 1994
SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk Gramegna, Alex; Giudici, Paolo Frontiers in Artificial Intelligence, Vol. 4 https://doi.org/10.3389/frai.2021.752558	journal	September 2021
The Best Way to Select Features? Comparing MDA, LIME, and SHAP Man, Xin; Chan, Ernest P. The Journal of Financial Data Science, Vol. 3, Issue 1 https://doi.org/10.3905/jfds.2020.1.047	journal	December 2020

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